In accordance with the conventional method the open and closed-loop controlling of industrial processes is based on a physical model designed to emulate the real industrial process. For the open-loop control of the cooling line of a hot-rolling mill the physical model should reflect as accurately as possible the reality of the overall cooling process. However there are now effects which are not accessible for precise model building since they are heavily system-specific or the effort required to describe them is not worthwhile. One example which might be cited here is the influence of the steel surface properties on the heat transfer.
To achieve model building which reflects reality as accurately as possible, the physical model is thus expanded by a statistical model which utilizes the available correlations of the widest variety of influencing factors with the model error for model correction. If for example it is established that the physical model, for a particular material with a particular width, thickness and speed, causes a typical error of 10° C. and a distribution of +/−20° C., the systematic component can be corrected with the downstream statistical model. This so-called statistical model correction can be performed in various ways. One method is to store, in what are known as inheritance tables, correction factors in material class, width class, temperature class and thickness class compartments etc. However this model reaches its limits if a greater number of input parameters is to be covered. For example if one has ten input variables and would like to form ten time intervals for each input variable, this produces 10 billion compartments of which only a small part will be filled in the lifecycle of the system. The lower the production is, the greater will be the number of empty compartments, which means that for production of a product with new parameter values the likelihood is great that there will not yet be any filled compartment, i.e. not yet any correction factor present for it and the statistical modeling thus comes to nothing.
A further method for generating a statistical model is neuronal networks. These are trained with the influencing variables as input and the desired correction factor as output. Since the neuronal network represents the correction factor as a constant function of the influencing variables, it is not possible to exclude the possibility, even with the inclusion of a neuronal networks, of a systematic error remaining with particular materials. A further disadvantage of neuronal networks is that they have to be comprehensively trained before they function in a useable way, i.e. a large number of strips have to be cooled before the neuronal network is ready for use. Until the training is completed there are no correction factors available.